NatureWave

Miles Van Denburg & Gabriel Yarleque

5/4/2020

Introduction

Objective:

Package: NatureWave

Acoustic Indices

Acoustic indices allow an aggregation of acoustic signals to represent the overall acoustic community diversity of the landscape.

The Normalized Difference Soundscape Index (NDSI) characterizes soundscape properties as such to allow the identification of human influence of the landscape through the partitioning of soundscapes into biophony (sounds from biodiversity) and anthrophony (sounds from humans).

This partitioning allows the classification of a particular location based on its ecological and anthropic characteristics.

NDSI = (biophony - anthrophony) / (biophony + anthrophony)

This project provides a less costly-reproducible approach to measurement and analysis and contributes to the applications of remote sensing technologies in conservation planning and monitoring of habitat quality.

Data

Statewide Massachusetts Assessment: November 2011 http://jamba.provost.ads.umass.edu/web/caps2011/CAPS2011data.htm

## Reading layer `sample_sites_projected' from data source `C:\Users\jyarlequeipanaque\Documents\R\win-library\3.6\naturewave\extdata\sample_sites_projected.shp' using driver `ESRI Shapefile'
## Simple feature collection with 11 features and 5 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 146419.9 ymin: 869709.6 xmax: 178745.5 ymax: 928660.8
## proj4string:    +proj=lcc +lat_1=41.71666666666667 +lat_2=42.68333333333333 +lat_0=41 +lon_0=-71.5 +x_0=200000 +y_0=750000 +datum=NAD83 +units=m +no_defs
## Reading layer `AOI' from data source `C:\Users\jyarlequeipanaque\Documents\R\win-library\3.6\naturewave\extdata\AOI.shp' using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 0 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -72.30321 ymin: 42.02124 xmax: -71.53417 ymax: 42.69707
## CRS:            4326

Methodology

Focal Mean

# # Focal Mean using circular focalWeight at a buffer of 2500 meters
# 
# #2500 Buffers
# 
# fw_sum_2500 <- lapply(landcover_list,function(x){
#   fw <- focalWeight(x, 2500, type='circle')
#   })
# 
# 
# #Runs focal at a buffer weight of 2500 m radius
# 
# radius3 <- 2500
# lc2500 <- lapply(1:4, function(x) {  # x <- 1
#   f <- paste0("../external/data/Amherst_CAPS2011", names(lc_stack)[x], "_", radius3, ".tif")
#   r <- focal(lc_stack[[x]], w = as.matrix(fw_sum_2500[[x]]), filename = f, overwrite = TRUE)
#   return(r)
# })
# lc2500_stacked <- stack(lc2500)  # Stack
# 
# 
# # lc2500_brick <- brick(lc2500_stacked) # Brick
# 
# # Plotting out the results
# tit3 <- "2500 (m) Buffer"
# par(mfrow = c(2, 2), mar = c(.5, 0, 2, 6))
# lapply(1:4, function(x){
#   plot(lc2500_stacked[[x]], axes = FALSE, box = FALSE, main = toupper(c(tit3, gsub(".tif", "", basename(f[x])))))
#   plot(st_geometry(sample_points_f), add = TRUE)
# })

Random Forest

# Create RandomForest Model
set.seed(100)
rf_2500 <- randomForest(NDSI ~., data=DS_2500_scaled, mtry= 2, importance = TRUE, na.action=na.omit)

randomForest::varImpPlot(rf_2500, main = "Variable Importance at 2500 buffer level")

Predictive Modeling

# Use raster::predict to predict NDSI values across the landscape
ndsi_pred <- raster::predict(object = new_stack, model=rf_2500, type = 'response', index = 5)
ndsi_pred

Results from Preliminary Work

Linear Modeling

Results from Focal Mean

Results from Random Forest

Variable Importance plot

##               %IncMSE IncNodePurity
## Imperv           6.69          0.10
## Connectedness    5.75          0.13
## Structure        5.54          0.11
## Traffic          9.61          0.11

Results from Predictive Modeling

NDSI Predictive surface

Conclusion and Discussion

Future work: